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End-to-end learning for combining multiple robot

actionsFebruary 14th, 2018

Tetsuya Ogata

Joint Appointed Fellow, AIST AI Research Center

Professor, Waseda University

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Cognitive Robotics- High caliculation

cost

- Redesign for each task

ComplexTask

Applicability

< Motion Generation Method for Robotics >Robotics with Deep Learning

[K.Yamazaki,2012]

[K.Noda, 2014]

- Low caliculation cost

- Same architecture

- Industrial robot

- Hight-DOF robot

[S.Levine, 2016]

Our Studies

[P.C.Yang, 2016]

Design model Guided Policy Learning

- Extensive search time

- Redesign for each task

- Experimentalrobot

- Low-DOF

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Teleoperation DCAE extract feature vector from raw image

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- 3D mouse and HMD

- Applicable to non-backdrivable robot

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MTRNNlearn sequencesby hierarchizing

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K. Kase, K. Suzuki, P. Yang, H. Mori, and T. Ogata: IEEE ICRA2018 (accepted)

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